2020 blocks are larger in the Oakland area, so 2020 was used for the shapefiles.
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|======================================================================| 100%## Warning in st_centroid.sf(.): st_centroid assumes attributes are constant over
## geometries of x##
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|======================================================================| 100%## Warning in st_centroid.sf(.): st_centroid assumes attributes are constant over
## geometries of x## Warning: Expected 4 pieces. Additional pieces discarded in 1053075 rows [11, 12,
## 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 27, 28, 29, 30, 31, ...].## Warning: Expected 4 pieces. Missing pieces filled with `NA` in 55425 rows [1, 2,
## 9, 72, 73, 80, 143, 144, 151, 214, 215, 222, 285, 286, 293, 356, 357, 364, 427,
## 428, ...].## Warning: Expected 3 pieces. Additional pieces discarded in 1365492 rows [11, 12,
## 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 27, 28, 29, 30, 31, ...].## Warning: Expected 3 pieces. Missing pieces filled with `NA` in 71868 rows [1, 2,
## 9, 73, 74, 81, 145, 146, 153, 217, 218, 225, 289, 290, 297, 361, 362, 369, 433,
## 434, ...].## Warning: attribute variables are assumed to be spatially constant throughout all
## geometriesFor the calculation of population change, I mapped the census blocks from 2010 to match the blocks of 2020, assuming proportional distribution of population within the census block areas.
I can’t quite figure out how to get the cookie cutter method to work.
density_change_oakland <- combined_oakland_blocks %>% select(block = GEOID20, perc_area, area_2020) %>% left_join( alameda_pop_race_2010, ) %>% mutate( pop_2010 = pop * perc_area, original_pop_2010 = pop, pop = NULL, race_2010 = race, race = NULL ) %>% left_join( alameda_pop_race_2020, by = c(“GEOID20” = “block”) ) %>% mutate( density_change = pop - pop_2010 )
Instead, I’ll just use matching GEOIDs for now and create the map.
## Warning: sf layer has inconsistent datum (+proj=longlat +datum=NAD83 +no_defs).
## Need '+proj=longlat +datum=WGS84'